There are many Internet or web based services that have a need to distinguish between a human and a computer user interacting with the service. For example, there are many free e-mails services that allow a user to create an e-mail account by merely entering some basic information. The user is then able to use the e-mail account to send and receive e-mails. This ease of establishing e-mail accounts has allowed spammers to produce computer programs to automatically create e-mail accounts with randomly generated account information and then employ the accounts to send out thousands of spam e-mails. Other Internet or web based services provide users with a convenient means through which to order products such as tickets, access personal account information, or to access other services. These web based systems are not only convenient to vendors as well as to their customers, but they also reduce overall costs.
Web based services have increasingly employed Turing test challenges (commonly known as a Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA™) or Human Interactive Proof (HIP)) in order distinguish between a human and a computer as the user of the web service. The HIP or CAPTCHA, which will be used interchangeably herein, is designed so that a computer program would have difficulty passing the test, but a human can more easily pass the test. The web service will only allow the user to employ the service after the user has passed the HIP.
One common example of an HIP is an image that includes text, which may be an actual word or phrase, or may be a nonsensical combination of letters, digits, and other characters. To solve the HIP challenge, a user types in the characters that are shown. Other types of challenges (e.g., audio and/or video challenges) may also be developed as HIPs, which are all designed to determine whether a particular request received by a web site is being initiated by a human being.
While current character-based HIPs can work very well in many applications, automated systems have become better at circumventing them through improved character recognition and image filtering and processing techniques. For example, in the case of a text-based HIP optical character recognitions (OCR) systems can allow an automated computer program to recognize at a fairly high percentage characters even with the distortions, convolutions, or noise that have been added to a text based challenge. Given this success rate of OCR, an automated system will achieve a pass rate for the HIP challenge that may not be acceptable to the service that is employing the HIP. Similarly for an image-based HIP, machine vision systems can provide fairly accurate classification of images and over many HIP challenges could achieve a substantial success rate. There is a continuing need to counter the success of automated computer programs that attempt to pass HIP challenges.